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LaunchDarkly MCP Server

LaunchDarkly MCP Server

by LaunchDarkly
Added to Onei
MIT
Repository Docs npm
Developer Tools

Summary

Local MCP server for LaunchDarkly — manage feature flags, AI Configs, environments, audit logs and code references from an agent.

Features

  • Full feature-flag lifecycle: status, list, create, patch and delete
  • Manage AI Configs including targeting and variations
  • List environments by project
  • Read audit-log entries to trace who changed which flag
  • List code repositories referencing flags, for safe flag cleanup
  • Runs locally for federal and EU environments where the hosted server is unavailable

Installation

Set up this MCP server in your favorite AI agent — copy a ready-made configuration below.

Any MCP-compatible agent

Most agents (Claude, Cursor, Windsurf, VS Code, and more) read a standard mcpServers configuration.

  1. Open your agent's MCP configuration file.
  2. Merge the snippet below into it, filling in the environment variables with your own values.
  3. Restart the agent — the "LaunchDarkly MCP Server" tools will be available.
{
  "mcpServers": {
    "launchdarkly": {
      "command": "npx",
      "args": [
        "-y",
        "--package",
        "@launchdarkly/mcp-server",
        "--",
        "mcp",
        "start",
        "--api-key",
        "$LD_ACCESS_TOKEN"
      ],
      "env": {
        "LD_ACCESS_TOKEN": "api-xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
      }
    }
  }
}

Description

LaunchDarkly publishes a local MCP server for customers who cannot use its hosted one — specifically its federal and European Union environments, where data residency or compliance rules rule out a vendor-hosted endpoint. Functionally it exposes the same feature-management surface, running on your own machine under your own credentials.

What it exposes. FeatureFlags — get flag status, list, create, patch and delete, which covers the full lifecycle an agent needs to roll a change out behind a flag rather than shipping it bare. AiConfigs — list, create, update and delete AI Configs along with their targeting and variations, LaunchDarkly's mechanism for managing model choice, prompts and parameters as configuration instead of as deployed code. Environments — list environments by project. AuditLog — list audit entries, so an agent can answer who changed which flag and when. CodeReferences — list repositories where flags are referenced, which is how you find the flags that are safe to clean up.

The AI Configs tooling is the part that has become interesting recently: it lets an agent adjust which model or prompt a feature is using, and to whom, without a deploy — the same targeting machinery teams already use for feature rollout, applied to model configuration.

Setup. Runs through npx from the @launchdarkly/mcp-server package, authenticated with an API key from LaunchDarkly's Authorization page (api- followed by a UUID). The key can be passed on the command line or, better, injected from an environment variable so it never lands in a checked-in config file. MIT-licensed.

Note: if you are on standard LaunchDarkly rather than the federal or EU environments, the hosted MCP server is the supported path and this local server is the fallback.

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